May 2025 arXiv papers — page 45

Domenico Ciuonzo, Alessio Zappone, Marco Di Renzo

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes th

Wenhu Li, Niki van Stein, Thomas Bäck, Elena Raponi

Bayesian optimization (BO) is a powerful class of algorithms for optimizing expensive black-box functions, but designing effective BO algorithms remains a manual, expertise-driven task. Recent advancements in Large Language Models (LLMs) have opened new avenues for automating scientific discovery, including the automatic design of optimization algorithms. Wh

Pie Desire Ebode Atanhgana

We extend the theory of circular game chromatic numbers to signed graphs by defining the invariant $\chi_c^g(G,\sigma)$ for signed graphs $(G,\sigma)$. Our analysis establishes tight bounds dependent on the structural properties of the underlying graph $G$ and its signature $\sigma$. Building on the foundational framework of Lin and Zhu \cite{LinZhu2009}, we

Seungmin Lee, Yongsang Yoo, Minhwa Jung, Min Song

Dialogue Topic Segmentation (DTS) aims to divide dialogues into coherent segments. DTS plays a crucial role in various NLP downstream tasks, but suffers from chronic problems: data shortage, labeling ambiguity, and incremental complexity of recently proposed solutions. On the other hand, Despite advances in Large Language Models (LLMs) and reasoning strategi

Nils Neukirch, Johanna Vielhaben, Nils Strodthoff

Internal representations are crucial for understanding deep neural networks, such as their properties and reasoning patterns, but remain difficult to interpret. While mapping from feature space to input space aids in interpreting the former, existing approaches often rely on crude approximations. We propose using a conditional diffusion model - a pretrained

Axel Richard, Pascal Brault, Nicolas Froloff, Olivier Aubry

This work establishes a protocol to study via Molecular Dynamics simulation the degradation of Per-and Polyfluoroalkyl Substances (PFAS) in water by hydroxyl radical. To achieve this, molecular dynamics simulations are carried out, using ReaxFF reactive interaction potential. Simulations are carried out under a temperature ramp for determining all possible p

Karl Lorensen, Johan Öinert

Let $R$ be a ring, $\sigma:R\to R$ a ring endomorphism, and $\delta:R\to R$ a $\sigma$-derivation. We establish that the Ore extension $R[x;\sigma,\delta]$ satisfies the rank condition if and only if $R$ does. In addition, we prove analogous results for the right and left strong rank conditions. However, in the right case, the ``if" part requires the hypothe

Zachary Munro, Daniel T. Wise

We define strict C(n) small-cancellation complexes, intermediate to C(n) and C(n+1), and we prove groups acting properly cocompactly on a simply-connected strict C(6) complex are hyperbolic relative to a collection of maximal virtually free abelian subgroups of rank 2. We study geometric walls in a simply-connected strict C(6) complex, and we use them to pro

Thierry Dana-Picard, Daniel Tsirkin

We explore offsets of Cayley ovals, by networking with different kinds of software. Using their specific abilities, algebraic, geometric, dynamic, we conjecture interesting properties of the offsets. For a given progenitor (the given plane curve whose offsets are studied), changes in the offset distance induce great changes in the shape and the topology of t

Zhipeng He, Chun Ouyang, Lijie Wen, Cong Liu

Adversarial attacks pose a significant threat to machine learning models by inducing incorrect predictions through imperceptible perturbations to input data. While these attacks are well studied in unstructured domains such as images, their behaviour on tabular data remains underexplored due to mixed feature types and complex inter-feature dependencies. This

Runze Lin, Junghui Chen, Biao Huang, Lei Xie

In the era of Industry 4.0 and smart manufacturing, process systems engineering must adapt to digital transformation. While reinforcement learning offers a model-free approach to process control, its applications are limited by the dependence on accurate digital twins and well-designed reward functions. To address these limitations, this paper introduces a n

Xinlei Yin, Xiulian Peng, Xue Jiang, Zhiwei Xiong

Target audio source separation with natural language queries presents a promising paradigm for extracting arbitrary audio events through arbitrary text descriptions. Existing methods mainly face two challenges, the difficulty in jointly modeling acoustic-textual alignment and semantic-aware separation within a blindly-learned single-stage architecture, and t

Charles London, Varun Kanade

Pause tokens, simple filler symbols such as "...", consistently improve Transformer performance on both language and mathematical tasks, yet their theoretical effect remains unexplained. We provide the first formal separation result, proving that adding pause tokens to constant-depth, logarithmic-width Transformers strictly increases their computational expr

Leonid L. Doskolovich, Artem I. Kashapov, Evgeni A. Bezus, Dmitry A. Bykov

We theoretically describe the optical computation of the divergence of a two-dimensional vector field, which is composed by the transverse electric field components of an incident light beam. The divergence is computed in reflection at oblique incidence of light on a layered structure. We show that in the particular case of a linearly polarized incident beam

Davide Rovere

The relation between covariant fracton gauge theory and Moller-Hayashi-Shirafuji theory of gravity is investigated. The former is the gauge theory of a rank-two symmetric tensor with gauge symmetry given by the double derivative of a scalar parameter; the latter is the most general theory, whose action is quadratic in the Weitzenb\"ock torsion. We show that

Marcus J. Vroemen, Yuqian Chen, Yui Lo, Tengfei Xue

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcellation, posing challenges for large-scale studies. We introduce DeepMultiConnectome, a deep-learning model that predicts structural connectomes directly from tractography, bypassing

Makoto Shimamura, Shingo Matsugaya, Keisuke Sakai, Kosuke Takeshige

While law enforcements agencies and cybercrime researchers are working hard, fake E-commerce scam is still a big threat to Internet users. One of the major techniques to victimize users is luring them by black-hat search-engine-optimization (SEO); making search engines display their lure pages as if these were placed on compromised websites and then redirect

Hadi Askari, Shivanshu Gupta, Fei Wang, Anshuman Chhabra

Pretrained Large Language Models (LLMs) achieve strong performance across a wide range of tasks, yet exhibit substantial variability in the various layers' training quality with respect to specific downstream applications, limiting their downstream performance. It is therefore critical to estimate layer-wise training quality in a manner that accounts for bot

Yuan Gao, Hao Wu, Fan Xu, Yanfei Xiang

Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing. Traditional autoregressive machine learning models often fail in these tasks as minor errors accumulate and lead to rapid forecast degradation. To address this problem, we propose NeuralOM, a general

Devran Ugurlu, Shuang Qian, Elliot Fairweather, Charlene Mauger

A cardiac digital twin is a virtual replica of a patient's heart for screening, diagnosis, prognosis, risk assessment, and treatment planning of cardiovascular diseases. This requires an anatomically accurate patient-specific 3D structural representation of the heart, suitable for electro-mechanical simulations or study of disease mechanisms. However, genera

Guillaume Moinard, Matthieu Latapy

We improve a flocking model on street networks introduced in a previous paper. We expand the field of vision of walkers, making the model more realistic. Under such conditions, we obtain groups of walkers whose gathering times and robustness to break ups are better than previous results. We explain such improvements because the alignment rule with vision gua

Agastya Raj, Daniel C. Kilper, Marco Ruffini

With the growing demand for high-bandwidth, low-latency applications, Optical Spectrum as a Service (OSaaS) is of interest for flexible bandwidth allocation within Elastic Optical Networks (EONs) and Open Line Systems (OLS). While OSaaS facilitates transparent connectivity and resource sharing among users, it raises concerns over potential network vulnerabil

Moritz Cygorek, Erik M. Gauger

The high numerical demands for simulating non-Markovian open quantum systems motivate a line of research where short-time dynamical maps are extrapolated to predict long-time behavior. The transfer tensor method (TTM) has emerged as a powerful and versatile paradigm for such scenarios. It relies on a systematic construction of a converging sequence of time-n

Sherry Mason, Tawfiq Ammari

This study examines how Reddit users engaged with the racial narratives of Lovecraft Country and Watchmen, two television series that reimagine historical racial trauma. Drawing on narrative persuasion and multistep flow theory, we analyze 3,879 Reddit comments using topic modeling and critical discourse analysis. We identify three dynamic social roles advoc

Razvan Gabriel Iagar, David Puertas-Centeno

We introduce new classes of informational functionals, called \emph{upper moments}, respectively \emph{down-Fisher measures}, obtained by applying classical functionals such as $p$-moments and the Fisher information to the recently introduced up or down transformed probability density functions. We extend some of the the most important informational inequali

Catherine Donati-Martin, Alain Rouault

In 1992, M. Wschebor proved a theorem on the convergence of small increments of the Brownian motion. Since then, it has been extended to various processes. We prove a version of this theorem for the Hermitian Brownian motion and the free Brownian motion. Since these theorems deal with a convergence to a deterministic limit, we prove also the convergence in d

Geetika, Somya Tyagi, Bapi Chatterjee

Instrumental variables (IV) analysis is an important applied tool for areas such as healthcare and consumer economics. For IV analysis in high-dimensional settings, the Generalized Method of Moments (GMM) using deep neural networks offers an efficient approach. With non-i.i.d. data sourced from scattered decentralized clients, federated learning is a popular

Liang Cheng, Zhaowei Wang, Mark Steedman

While fine-tuning LLMs on NLI corpora improves their inferential performance, the underlying mechanisms driving this improvement remain largely opaque. In this work, we conduct a series of experiments to investigate what LLMs actually learn during fine-tuning. We begin by analyzing predicate frequencies in premises and hypotheses across NLI datasets and iden

Sabbir Ahmed, Mamshad Nayeem Rizve, Abdullah Al Arafat, Jacqueline Liu

Semi-Supervised Federated Learning (SSFL) is gaining popularity over conventional Federated Learning in many real-world applications. Due to the practical limitation of limited labeled data on the client side, SSFL considers that participating clients train with unlabeled data, and only the central server has the necessary resources to access limited labeled

Pierre Gaspard

Active suspensions composed of self-propelled colloidal particles are considered. Their propulsion of is generated by chemical reactions occurring by heterogeneous catalysis and diffusiophoresis coupling the concentration gradients of reacting molecular species to the fluid velocity. By this mechanism, chemical free energy is transduced into mechanical motio

Matteo Nardelli, Francesco De Sclavis, Michela Iezzi

Crypto-assets and central bank digital currencies (CBDCs) are reshaping how value is exchanged in distributed computing environments. These systems combine cryptographic primitives, protocol design, and system architectures to provide transparency and efficiency while raising critical challenges around privacy and regulatory compliance. This survey offers a

Afaf Taik, Khaoula Chehbouni, Golnoosh Farnadi

Fairness in federated learning has emerged as a rapidly growing area of research, with numerous works proposing formal definitions and algorithmic interventions. Yet, despite this technical progress, fairness in FL is often defined and evaluated in ways that abstract away from the sociotechnical contexts in which these systems are deployed. In this paper, we

Wang Dinghuai, Yin Huicheng

In this paper, through the introduction of partial multiple weights, we firstly study the related Rubio de Francia extrapolation theorem within the framework of partial Muckenhoupt classes and further obtain the corresponding extrapolation theorem for two types of off-diagonal estimates. Secondly, we establish some weighted estimates for fractional integrals

Camille Delavaux, Thomas Crowther, James Bever, Evan Gora

In our original paper (Delavaux et al. 2024; https://www.nature.com/articles/s41586-024-07110-y), we find that the latitudinal diversity gradient (LDG) in plant species richness is reduced on oceanic islands worldwide. Moreover, we find that plants that associate with mutualists, including pollinators, AM fungi, and N-fixing bacteria, disproportionately cont

Fengzhe Zhang, Laurence I. Midgley, José Miguel Hernández-Lobato

Score-based diffusion models (SBDMs) are powerful amortized samplers for Boltzmann distributions; however, imperfect score estimates bias downstream Monte Carlo estimates. Classical importance sampling (IS) can correct this bias, but computing exact likelihoods requires solving the probability-flow ordinary differential equation (PF-ODE), a procedure that is

Lixing He, Yunqi Guo, Zhenyu Yan, Guoliang Xing

In crowded places such as conferences, background noise, overlapping voices, and lively interactions make it difficult to have clear conversations. This situation often worsens the phenomenon known as "cocktail party deafness." We present ClearSphere, the collaborative system that enhances speech at the conversation level with multi-earphones. Real-time conv

Yifei Wang, Yu Sheng, Linjing Li, Daniel Zeng

Recent advances in handling long sequences have facilitated the exploration of long-context in-context learning (ICL). While much of the existing research emphasizes performance improvements driven by additional in-context examples, the influence on the trustworthiness of generated responses remains underexplored. This paper addresses this gap by investigati

Mustafa İzzet Muştu, Hazım Kemal Ekenel

The increasing prevalence of computer vision applications necessitates handling vast amounts of visual data, often containing personal information. While this technology offers significant benefits, it should not compromise privacy. Data privacy regulations emphasize the need for individual consent for processing personal data, hindering researchers' ability

Yitai Huang, Weidong Mei, Xin Wei, Zhi Chen

Movable antenna (MA) has attracted increasing attention in wireless communications due to its capability of wireless channel reconfiguration through local antenna movement within a confined region at the transmitter/receiver. However, to determine the optimal antenna positions, channel state information (CSI) within the entire region, termed small-scale chan

Andrew Urilyon, Leonardo Biagetti, Jitendra Kethepalli, Jacopo De Nardis

One-dimensional integrable and quasi-integrable systems display, on macroscopic scales, a universal form of transport known as Generalized Hydrodynamics (GHD). In its standard Euler-scale formulation, GHD mirrors the equations of a two-dimensional compressible fluid but ignores fluctuations and becomes numerically unwieldy as soon as integrability-breaking p

Evgeny Khukhro

It is proved that if a finite group $G$ has an automorphism of order $n$ with $m$ fixed points, then $G$ has a soluble subgroup whose index and Fitting height are bounded in terms of $m$ and $n$. As a corollary, a problem of B. Hartley is solved in the affirmative: if a locally finite group $G$ has an element with finite centralizer, then $G$ has a subgroup

Sen Bai, Chunqi Yang, Xin Bai, Xin Zhang

Binary (0-1) integer programming (BIP) is pivotal in scientific domains requiring discrete decision-making. As the advance of AI computing, recent works explore neural network-based solvers for integer linear programming (ILP) problems. Yet, they lack scalability for tackling nonlinear challenges. To handle nonlinearities, state-of-the-art Branch-and-Cut sol

Zheng Qin, Tian-Yang Sun, Bo-Yuan Li, Jing-Fei Zhang

When gravitational waves (GWs) propagate near massive objects, they undergo gravitational lensing that imprints lens model dependent modulations on the waveform. This effect provides a powerful tool for cosmological and astrophysical studies. Due to the added parameters of lenses and the uncertainty of lens models, parameter inference for lensed GW events us

Justin J. H. Lo, Patrycja Strycharczuk, Sam Kirkham

The way speakers articulate is well known to be variable across individuals while at the same time subject to anatomical and biomechanical constraints. In this study, we ask whether articulatory strategy in vowel production can be sufficiently speaker-specific to form the basis for speaker discrimination. We conducted Generalised Procrustes Analyses of tongu

Nadia Benlakhouy, Ahmed Jellal, Hocine Bahlouli, Pablo Díaz

We study the Hofstadter butterfly spectrum in (1+3+1) chirally twisted multilayer graphene (CTMLG) subject to perpendicular magnetic field and light with different polarizations. We focus on the interplay between twist angles and light-induced effects. In equilibrium, we examine symmetric ($\theta_1 = \theta_2$) and asymmetric ($\theta_1 \neq \theta_2$) conf

Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu

Large Language Models (\textbf{LLMs}), e.g. ChatGPT, have been widely adopted in real-world dialogue applications. However, LLMs' robustness, especially in handling long complex dialogue sessions, including frequent motivation transfer, sophisticated cross-turn dependency, is criticized all along. Nevertheless, no existing benchmarks can fully reflect these

Jie Shao, Jianxin Wu

Despite the impressive performance of large language models (LLMs), the process of endowing them with new capabilities--such as mathematical reasoning--remains largely empirical and opaque. A critical open question is whether reasoning abilities stem from the entire model, specific modules, or are merely artifacts of overfitting. In this work, we hypothesize

Meng Qin, Jiahong Liu, Irwin King

Graph neural networks (GNNs), which capture graph structures via a feature aggregation mechanism following the graph embedding framework, have demonstrated a powerful ability to support various tasks. According to the topology properties (e.g., structural roles or community memberships of nodes) to be preserved, graph embedding can be categorized into identi

Peng Liu, Liang Chen, Minru Bai

As an extension of the alternating direction method of multipliers (ADMM), the semi-proximal ADMM (sPADMM) has been widely used in various fields due to its flexibility and robustness. In this paper, we first show that the two-block sPADMM algorithm can achieve an $O(1/\sqrt{K})$ non-ergodic convergence rate. Then we propose an accelerated sPADMM (AsPADMM) a

Ling Liu, Yihua Dong, Qijun Ye, Xin-Zheng Li

Lee-Yang phase transition theory is a milestone in statistical physics. Its applications in realistic systems, however, had been substantially hindered by availability of practical schemes to calculate the Lee-Yang zeros. In this manuscript, we extend the scheme we have designed earlier [Phys. Rev. E 109, 024118 (2024)] and report simulation results for the

Diego Córdoba, Andrés Laín-Sanclemente, Luis Martínez-Zoroa

We establish the existence of compactly supported solutions of the inviscid incompressible 2D Boussinesq equation with $C^{1,\sqrt{\frac{4}{3}}-1-\varepsilon}\cap L^{2}$ force that develop a singularity in finite time. Importantly, the force preserves this regularity at the blow-up time. Moreover, the forces in the vorticity and density equations have compac

Jiahan Chen, Da Li, Keping Bi

In recent years, sharing lifelogs recorded through wearable devices such as sports watches and GoPros, has gained significant popularity. Lifelogs involve various types of information, including images, videos, and GPS data, revealing users' lifestyles, dietary patterns, and physical activities. The Lifelog Semantic Access Task(LSAT) in the NTCIR-18 Lifelog-

Zhongwang Pang, Chunyi Li, Hongfei Dai, Wenlin Li

With the continuous advancement of laser technology, accurately evaluating the noise spectrum of high-performance lasers has become increasingly challenging. In this work, we demonstrate a high-precision laser spectrum analyzer based on the proposed digital decoherence method, which can precisely measure the frequency noise spectrum of sub-Hz linewidth laser

Mustafa İzzet Muştu, Hazım Kemal Ekenel

The increasing demand for large-scale visual data, coupled with strict privacy regulations, has driven research into anonymization methods that hide personal identities without seriously degrading data quality. In this paper, we explore the potential of face swapping methods to preserve privacy in video data. Through extensive evaluations focusing on tempora

Minghao Han, Weiyi You, Jinhua Zhang, Leheng Zhang

While learned image compression (LIC) focuses on efficient data transmission, generative image compression (GIC) extends this framework by integrating generative modeling to produce photo-realistic reconstructed images. In this paper, we propose a novel diffusion-based generative modeling framework tailored for generative image compression. Unlike prior diff

Emmanuel Floratos, Kimon Manolas, Ioannis Tsohantjis

Unitary metaplectic representations of the group $SL_2(\mathbb{Z}_{2^n})$ are necessary to describe the evolution of $2^n$-dimensional quantum systems, such as systems involving $n$ qubits. It is shown that in order for the metaplectic property to be fulfilled, an increase in the dimensionality of the involved $n$-qubit Hilbert spaces, from $2^n$ to $2^{2n}$

Sourav Biswas, Debadri Bhattacharjee, Pradip Kumar Chattopadhyay

The current model explores spherically symmetric anisotropic compact stars within the Rastall theory of gravity. By employing the Krori and Barua metric ansatz (K.D. Krori and J. Barua, J. Phys. A: Math. Gen. 8 (1975) 508), we derive a set of tractable, singularity-free relativistic solutions to the Einstein field equations. Using a best-fit equation for the

Evgeny Akhmedov

It has been claimed in a number of publications that neutrinos can exhibit chirality oscillations. In this note we discuss the notion of chirality and show that chiral neutrino oscillations in vacuum do not occur. We argue that the incorrect claims to the contrary resulted from a failure to clearly discriminate between quantum fields, states and wave functio

Cainan Davidson, Deva Ramanan, Neehar Peri

Autonomous Vehicles (AVs) collect and pseudo-label terabytes of multi-modal data localized to HD maps during normal fleet testing. However, identifying interesting and safety-critical scenarios from uncurated driving logs remains a significant challenge. Traditional scenario mining techniques are error-prone and prohibitively time-consuming, often relying on

Michał Czuba, Mateusz Stolarski, Adam Piróg, Piotr Bielak

Identifying super-spreaders can be framed as a subtask of the influence maximisation problem. It seeks to pinpoint agents within a network that, if selected as single diffusion seeds, disseminate information most effectively. Multilayer networks, a specific class of heterogeneous graphs, can capture diverse types of interactions (e.g., physical-virtual or pr

Tongyu Lu, Charlotta-Marlena Geist, Jan Melechovsky, Abhinaba Roy

We propose MelodySim, a melody-aware music similarity model and dataset for plagiarism detection. First, we introduce a novel method to construct a dataset focused on melodic similarity. By augmenting Slakh2100, an existing MIDI dataset, we generate variations of each piece while preserving the melody through modifications such as note splitting, arpeggiatio

Hui-Hui Duan, Yong-Lu Liu, Qin Chang, Ming-Qiu Huang

This paper investigates the double strangeness heavy flavor baryons $\Omega_c^0$ and $\Omega_b^-$, which contain two strange quarks. Using QCD light-cone sum rules (LCSRs), we calculate the form factors for the Cabibbo-suppressed processes $\Omega_c^0\to\Xi^-$ and $\Omega_b^-\to\Xi^0$, corresponding to the heavy-quark transitions $c\to d$ and $b\to u$, respe

Yu Zhang, Jinlong Ma, Yongshuai Hou, Xuefeng Bai

Multi-modal large language models (MLLMs) have achieved remarkable success on complex multi-modal tasks. However, it remains insufficiently explored whether they exhibit $\textbf{modality preference}$, a tendency to favor one modality over another when processing multi-modal contexts. To study this question, we introduce $\textbf{MC\textsuperscript{2}}$ benc

Peiming Guo, Meishan Zhang, Jianling Li, Min Zhang

Cross-domain constituency parsing is still an unsolved challenge in computational linguistics since the available multi-domain constituency treebank is limited. We investigate automatic treebank generation by large language models (LLMs) in this paper. The performance of LLMs on constituency parsing is poor, therefore we propose a novel treebank generation m

Shamil Ayupov, Maksim Nakhodnov, Anastasia Yaschenko, Andrey Kuznetsov

Personalized diffusion models have shown remarkable success in Text-to-Image (T2I) generation by enabling the injection of user-defined concepts into diverse contexts. However, balancing concept fidelity with contextual alignment remains a challenging open problem. In this work, we propose an RL-based approach that leverages the diverse outputs of T2I models

Christopher Knievel, Alexander Bernhardt, Christian Bernhardt

Intelligent tutoring systems combined with large language models offer a promising approach to address students' diverse needs and promote self-efficacious learning. While large language models possess good foundational knowledge of electrical engineering basics, they remain insufficiently capable of addressing specific questions about electrical circuits. I

Consuelo Martinez, Olivier Mathieu, Efim Zelmanov

According to V. Kac and J. van de Leur, the superconformal algebras are the simple $\Z$-graded Lie superalgebras of growth one which contains the Witt algebra. We describe an explicit classification of all cuspidal modules over the known supercuspidal algebras of rank $\geq 1$, and their central extensions. Our approach reveals some unnoticed phenomena. Inde

Keheliya Gallaba, Ali Arabat, Dayi Lin, Mohammed Sayagh

Foundation Models (FMs) have shown remarkable capabilities in various natural language tasks. However, their ability to accurately capture stakeholder requirements remains a significant challenge for using FMs for software development. This paper introduces a novel approach that leverages an FM-powered multi-agent system called AlignMind to address this issu

Sen Bai, Chunqi Yang, Xin Bai, Xin Zhang

Along with AI computing shining in scientific discovery, its potential in the combinatorial optimization (CO) domain has also emerged in recent years. Yet, existing unsupervised neural network solvers struggle to solve $k$-grouping problems (e.g., coloring, partitioning) on large-scale graphs and hypergraphs, due to limited computational frameworks. In this

Xiangqing Shen, Fanfan Wang, Rui Xia

LLMs have demonstrated remarkable capabilities in complex reasoning tasks, yet they often suffer from hallucinations and lack reliable factual grounding. Meanwhile, knowledge graphs (KGs) provide structured factual knowledge but lack the flexible reasoning abilities of LLMs. In this paper, we present Reason-Align-Respond (RAR), a novel framework that systema

Joonkyu Kim, Yejin Kim, Jy-yong Sohn

In continual learning scenarios, catastrophic forgetting of previously learned tasks is a critical issue, making it essential to effectively measure such forgetting. Recently, there has been growing interest in focusing on representation forgetting, the forgetting measured at the hidden layer. In this paper, we provide the first theoretical analysis of repre

Nawshin Mannan Proma, Victoria J Hodge, Rob Alexander

The safety of autonomous systems in dynamic and hazardous environments poses significant challenges. This paper presents a testing approach named SCALOFT for systematically assessing the safety of an autonomous aerial drone in a mine. SCALOFT provides a framework for developing diverse test cases, real-time monitoring of system behaviour, and detection of sa

Fabio Riva, Gioele Janett, Luca Belluzzi, Tanausú del Pino Alemán

Aims. The main goal of this paper is to present an accurate and efficient numerical strategy for solving the radiative transfer problem for polarised radiation in strong resonance lines forming out of local thermodynamic equilibrium, taking angle-dependent (AD) partial frequency redistribution (PRD) effects and J-state interference into account. We consider

Jiarui Zhang, Zhihao Li, Chong Wang, Bihan Wen

Neural fields (NFs) have achieved remarkable success in scene reconstruction and novel view synthesis. However, existing NF approaches that rely on RGB or LiDAR inputs often struggle under adverse weather conditions, limiting their robustness in real-world outdoor environments such as autonomous driving. In contrast, millimeter-wave radar is inherently resil

Zhibo Wang, Xiaoze Jiang, Zhiheng Qin, Enyun Yu

Query auto-completion (QAC) plays a crucial role in modern search systems. However, in real-world applications, there are two pressing challenges that still need to be addressed. First, there is a need for hierarchical personalized representations for users. Previous approaches have typically used users' search behavior as a single, overall representation, w

A. Rubio, J. M. Montanero, M. Vakili, F. H. M. Koua

We have produced superstable compound liquid microjets with a three-dimensional printed coaxial flow-focusing injector. The aqueous jet core is surrounded by a shell, a few hundred nanometers in thickness, of a low-concentration aqueous solution of a low-molecular-weight polymer. Due to the stabilizing effect of the polymeric shell, the minimum liquid flow r

Mehdi Bennis, Salem Lahlou

The trajectories of 6G and AI are set for a creative collision. However, current visions for 6G remain largely incremental evolutions of 5G, while progress in AI is hampered by brittle, data-hungry models that lack robust reasoning capabilities. This paper argues for a foundational paradigm shift, moving beyond the purely technical level of communication tow

Felix Krejca, Tobias Kietreiber, Alexander Buchelt, Sebastian Neumaier

The increasing volume of online discussions requires advanced automatic content moderation to maintain responsible discourse. While hate speech detection on social media is well-studied, research on German-language newspaper forums remains limited. Existing studies often neglect platform-specific context, such as user history and article themes. This paper a

Nikos Giannakakis, Argyris Manetas, Panagiotis P. Filntisis, Petros Maragos

Learning visual representations from observing actions to benefit robot visuo-motor policy generation is a promising direction that closely resembles human cognitive function and perception. Motivated by this, and further inspired by psychological theories suggesting that humans process scenes in an object-based fashion, we propose an object-centric encoder

Yiyuan Yang, Shitong Xu, Niki Trigoni, Andrew Markham

Sound source localization (SSL) is a critical technology for determining the position of sound sources in complex environments. However, existing methods face challenges such as high computational costs and precise calibration requirements, limiting their deployment in dynamic or resource-constrained environments. This paper introduces a novel 3D SSL framewo

Dario Ascari, Jonathan Fruchter

Let $G$ be a hyperbolic group that splits as a graph of free groups with cyclic edge groups. We prove that, unless $G$ is isomorphic to a free product of free and surface groups, every finite abelian group $M$ appears as a direct summand in the abelianization of some finite-index subgroup $G'\le G$. As an application, we deduce that free products of free and

Bertram Højer, Terne Sasha Thorn Jakobsen, Anna Rogers, Stefan Heinrich

Despite the widespread use of ''artificial intelligence'' (AI) framing in Natural Language Processing (NLP) research, it is not clear what researchers mean by ''intelligence''. To that end, we present the results of a survey on the notion of ''intelligence'' among researchers and its role in the research agenda. The survey elicited complete responses from 30

Shubham Singh Paliwal, Arushi Jain, Monika Sharma, Vikram Jamwal

Textual content in images is crucial in e-commerce sectors, particularly in marketing campaigns, product imaging, advertising, and the entertainment industry. Current text-to-image (T2I) generation diffusion models, though proficient at producing high-quality images, often struggle to incorporate text accurately onto complex surfaces with varied perspectives

Veekesh Kumar, Riccardo Tosi

Let $\alpha_1,\alpha_2$ be non-zero algebraic numbers such that $\frac{\log \alpha_2}{\log\alpha_1}\notin\mathbb{Q}$ and let $\beta$ be a quadratic irrational number. In this article, we prove that the values of two relatively prime polynomials $P(x,y,z)$ and $Q(x,y,z)$ with integer coefficients are not too small at the point $\left(\frac{\log\alpha_2}{\log

Shiqi Zhang, Tuomas Virtanen

Bioacoustic sound event detection (BioSED) is crucial for biodiversity conservation but faces practical challenges during model development and training: limited amounts of annotated data, sparse events, species diversity, and class imbalance. To address these challenges efficiently with a limited labeling budget, we apply the mismatch-first farthest-travers

Wouter G. J. van Zeist, Jan van Roestel, Gijs Nelemans, Jan J. Eldridge

Context: Studies of the Galactic population of double white dwarfs (DWDs) that would be detectable in gravitational waves by LISA have found differences in the number of predicted detectable DWDs of more than an order of magnitude, depending on the binary stellar evolution model used. Particularly, the binary population synthesis code BPASS predicts 20 to 40

Stefano Gramigna

BESIII (BEijing Spectrometer III) is a particle physics experiment with a vast physics program centered around the study of charmonium and the $\tau$ lepton. The performance of the spectrometer's inner tracker, the innermost part of a large drift chamber, has been degrading due to aging phenomena related to the large particle rate. Planned upgrades to the BE

Zelin Zang, Fei Wang, Liangyu Li, Jinlin Wu

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Recent UDA methods based on Vision Transformers (ViTs) have achieved strong performance through attention-based feature alignment. However, we identify a key limitation: foreground object mismatch, where the discrepancy in foreground ob

Naiyu Fang, Zheyuan Zhou, Kang Wang, Ruibo Li

Camera-based 3D semantic occupancy prediction offers an efficient and cost-effective solution for perceiving surrounding scenes in autonomous driving. However, existing works rely on explicit occupancy state inference, leading to numerous incorrect feature assignments, and insufficient samples restrict the learning of occupancy class inference. To address th

Yufeng Wang, Yiguang Bai, Tianqing Zhu, Ismail Ben Ayed

Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends trad

Maria Teresa Arias, Davide Barbieri, Eugenio Hernández

Scattering Networks were initially designed to elucidate the behavior of early layers in Convolutional Neural Networks (CNNs) over Euclidean spaces and are grounded in wavelets. In this work, we introduce a scattering transform on an arbitrary finite group (not necessarily abelian) within the context of group-equivariant convolutional neural networks (G-CNNs

Andrea Giovanni Nuzzolese

Knowledge Graphs (KGs) are increasingly adopted as a foundational technology for integrating heterogeneous data in domains such as climate science, cultural heritage, and the life sciences. Declarative mapping languages like R2RML and RML have played a central role in enabling scalable and reusable KG construction, offering a transparent means of transformin

Yisen Gao, Jiaxin Bai, Tianshi Zheng, Qingyun Sun

Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such as clinical diagnosis and scientific discovery. However, due to a lack of controllability, a single observation may yield numerous plausible but redundant or irrelevant hypotheses on large-scale knowledge graphs.

Lorenzo Monti, Tatiana Muraveva, Alessia Garofalo, Gisella Clementini

RR Lyrae stars (RRLs) are old pulsating variables widely used as metallicity tracers due to the correlation between their metal abundances and light curve morphology. With ESA Gaia DR3 providing light curves for about 270,000 RRLs, there is a pressing need for scalable methods to estimate their metallicities from photometric data. We introduce a unified deep

Caner Tanış, Yasin Asar

In this paper, we gain the new almost unbiased Liu-type estimators to literature for the Bell regression model. We provide the superiority of the proposed estimator to its competitors such as the maximum likelihood estimator and Liu-type estimators via some theorems. We also design an extensive Monte Carlo simulation study to show that the proposed estimator

Xihuan Lin, Jie Zhang, Gelei Deng, Tianzhe Liu

Incident response plays a pivotal role in mitigating the impact of cyber attacks. In recent years, the intensity and complexity of global cyber threats have grown significantly, making it increasingly challenging for traditional threat detection and incident response methods to operate effectively in complex network environments. While Large Language Models

Anand Brahmbhatt, Gon Buzaglo, Sofiia Druchyna, Elad Hazan

We propose a new method for the problem of controlling linear dynamical systems under partial observation and adversarial disturbances. Our new algorithm, Double Spectral Control (DSC), matches the best known regret guarantees while exponentially improving runtime complexity over previous approaches in its dependence on the system's stability margin. Our key

Viviana Giunzioni, Adrien Merlini, Francesco P. Andriulli

The use of boundary integral equations in modeling boundary value problems-such as elastic, acoustic, or electromagnetic ones-is well established in the literature and widespread in practical applications. These equations are typically solved numerically using boundary element methods (BEMs), which generally provide accurate and reliable solutions. When the

Yaohua Zha, Yanzi Wang, Hang Guo, Jinpeng Wang

Applying pre-trained models to assist point cloud understanding has recently become a mainstream paradigm in 3D perception. However, existing application strategies are straightforward, utilizing only the final output of the pre-trained model for various task heads. It neglects the rich complementary information in the intermediate layer, thereby failing to

Yuka Kotorii, Sonia Mahmoudi, Elisabetta A. Matsumoto, Ken'ichi Yoshida

A periodic tangle is a one-dimensional submanifold in $\mathbb{R}^3$ that has translational symmetry in one, two or three transverse directions. A periodic tangle can be seen as the universal cover of a link in the solid torus, the thickened torus, or the three-torus, respectively. Our goal is to study equivalence relations of such periodic tangles. Since al